docs: def plot(self, fig=None, iabscissa=1, iteridx=None,
               plot_mean=False, # was: plot_mean=True
               foffset=1e-19, x_opt=None, fontsize=9):
          """plot data from a `CMADataLogger` (using the files written 
          by the logger).
  
          Arguments
          ---------
              `fig`
                  figure number, by default 325
              `iabscissa`
                  ``0==plot`` versus iteration count,
                  ``1==plot`` versus function evaluation number
              `iteridx`
                  iteration indices to plot
  
          Return `CMADataLogger` itself.
  
          Examples
          --------
          ::
  
              import cma
              logger = cma.CMADataLogger()  # with default name
              # try to plot the "default logging" data (e.g.
              #   from previous fmin calls, which is essentially what
              #   also cma.plot() does)
              logger.plot()
              cma.savefig('fig325.png')  # save current figure
              logger.closefig()
  
          Dependencies: matlabplotlib/pyplot.
  
          """
          try:
              # pyplot: prodedural interface for matplotlib
              from matplotlib.pyplot import figure, subplot, hold, gcf
          except ImportError:
              ImportError('could not find matplotlib.pyplot module, function plot() is not available')
              return
  
          if fig is None:
              fig = 325
          if iabscissa not in (0, 1):
              iabscissa = 1
  
          self.load()  # better load only conditionally?
          dat = self
          dat.x = dat.xmean  # this is the genotyp
          if not plot_mean:
              if len(dat.x) < 2:
                  print('not enough data to plot recent x')
              else:
                  dat.x = dat.xrecent
  
          # index out some data
          if iteridx is not None:
              self.select_data(iteridx)
  
          if len(dat.f) <= 1:
              print('nothing to plot')
              return
  
          # not in use anymore, see formatter above
          # xticklocs = np.arange(5) * np.round(minxend/4., -int(np.log10(minxend/4.)))
  
          # dfit(dfit<1e-98) = NaN;
  
          # TODO: if abscissa==0 plot in chunks, ie loop over subsets where
          # dat.f[:,0]==countiter is monotonous
  
          figure(fig)
          self._enter_plotting(fontsize)
          self.fighandle = gcf()  # fighandle.number
  
          subplot(2, 2, 1)
          self.plot_divers(iabscissa, foffset)
          pyplot.xlabel('')
  
          # Scaling
          subplot(2, 2, 3)
          self.plot_axes_scaling(iabscissa)
  
          # spectrum of correlation matrix
          figure(fig)
  
          subplot(2, 2, 2)
          if plot_mean:
              self.plot_mean(iabscissa, x_opt)
          else:
              self.plot_xrecent(iabscissa, x_opt)
          pyplot.xlabel('')
          # pyplot.xticks(xticklocs)
  
          # standard deviations
          subplot(2, 2, 4)
          self.plot_stds(iabscissa)
          self._finalize_plotting()
          return self